Although some notable diseases, such as cystic fibrosis, are caused by mutations in a single gene, most complex diseases, such as diabetes or osteoporosis, develop through the interaction of multiple genes. However, in the search for genes that cause complex diseases, testing biological hypotheses has been limited by both statistical methods and computational power. This limitation is especially apparent when two or more genes are likely to interact synergistically—for example, if the gene encodes ligand-receptor pairs or other proteins that function in the same biochemical pathway. Genome-wide genotyping can now provide hundreds of thousands of data points on genetic polymorphisms with which to test the hypotheses that any of the genes they represent is associated with disease. But testing for all of possible synergies has been blocked by the computational demands of conducting the enormous number of statistical tests required even for pairs of genetic variations (much less for trios or higher-order interactions). Li et al. present a method for proceeding in a stepwise fashion through the myriad statistical associations that include both direct effects (of a single genetic variation acting alone) and genetic interactions among multiple genetic variants.

Although methods such as the one developed by Li et al. can elucidate important players in the genetic cause of disease, testing multiple combinations of genetic variants also runs the risk of increasing type I error—the chance that a significant association (but one that is not reflective of true biology) will be found. Li et al. therefore focus on maximizing the sensitivity and specificity of their method using simulation studies designed to detect all true single effects as well as two- to four-locus interactions. They considered interactions that were additive (the true effect of variants taken together is greater or less than the sum of the effects of each of the variants alone), multiplicative (the effect of variants is greater or less than the product of their individual effects), or threshold (the effect of one variant is only seen when a certain variant at another locus is present). The ability to detect such interactions in large data sets has the potential to return biologically plausible and novel information about disease processes in the exquisitely complex reality of human populations.